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Maryam Shakiba
Assistant Professor, Smead Aerospace
Friday, Sept. 15 | 10:40 a.m. | AERO 120

Abstract: This presentation discusses computational modeling of complex materials behaviors under multi-physics conditions for Aerospace applications. We develop chemistry, physics, and mechanics-based constitutive equations to explain complex systems. Then, we devise high-fidelity numerical ap-proaches and mechanistic machine learning to solve our problems.

The first part of the presentation focuses on progressive damage in fiber-reinforced composites. In such composites, cracks initiate around the fibers aligned transversely to the loading direction. The transverse cracks can cause leakage in specific applications or progress to inter-ply delamination and catastrophic failure. We integrate an efficient numerical framework with robust and accurate constitutive equations to study transverse behavior and multiple cracking of two-dimensional representations of fiber-reinforced composite laminates. We then develop deep learning frameworks to predict the elastic and post-failure full-field stress distribution and the crack pattern in two-dimensional representations of the composites based on their microstructures.

The second part of the presentation focuses on developing chemistry, physics, and mechanics-based constitutive equations to predict the stress and brittle failure responses of polymeric materials under multi-physics degradation. We connect the changes in the macromolecular network of materials due to multi-physics conditioning to their mechanical responses. The changes in the macromolecular network are obtained based on chemical characterization tests. The obtained constitutive equations predict the stress-strain response until failure with phase-field to capture the induced brittle failure. The constitutive equations are verified versus independent mechanical tests available in the literature for different types of materials.

Bio: Maryam Shakiba is an assistant professor at the Aerospace Engineering Sciences Department at the University of ÃÛÌÇÖ±²¥ Boulder. Before joining ÃÛÌÇÖ±²¥ Boulder, she was and assistant professor at Virginia Tech and a Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign. She received her Ph.D. from Texas A&M University and her B.S. and M.S. degrees from Tehran Polytechnic. Shakiba's group develops physics, chemistry, and mechanics-based constitutive equations to simulate multi-physics conditions for different advanced materials. The group also devises high-fidelity as well as mechanistic machine-learning approaches to solve engineering problems. Our goals are to (1) de-velop theoretical frameworks to understand advanced material responses under extreme multi-factor conditions and (2) integrate the theoretical framework with machine learning approaches, as physics-based machine learning is the key technology to creating true digital twins. Shakiba is the recipient of the AFOSR Young Investigator Program (YIP) award to investigate additively manufactured compos-ites for high-temperature applications and the NSF CAREER award to understand the multi-physics mechanisms that cause macroplastics fragmentation and generate microplastics.